The use of Artificial Neural Network for lipid and glycaemic profiles quantification through infrared spectroscopy
Resumo
This paper aims to look at the viability of the use of artificial neural networks to solve nonlinear correlations between infrared spectra and biochemical quantification tests, to build a computational system to predict the levels of glycaemic and lipid profiles using infrared spectroscopy. The studies of one of the parameters was modelled and showed signs of viability to quantify all parameters with the suggested methodology. Therefore, more complex and larger data sets are going to be tested with this technique.
Referências
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Irudayaraj, J. (2002). Rapid determination of vitamin C by NIR, MIR and FT-Raman techniques. J. Pharm. Pharmacol., London, v.54, n.9, p.1247-1255, 2002.
Jessen, T. E., Hoskuldsson, A. T., Bjerrum, P. J., Verder, H., Sorensen, L., Bratholm, P. S., . . . Jensen, M. A. (2014). Simultaneous determination of glucose, triglycerides, urea, cholesterol, albumin and total protein in human plasma by Fourier transform infrared spectroscopy: direct clinical biochemistry without reagents. Clin Biochem, 47(13-14), 1306-1312. doi: 10.1016/j.clinbiochem.2014.05.064
Karmowski, K. A. S., Jacek Majda, Piotr Rubisz, Stanisław Han e Andrzej Karmowski. (2014). Lipid index changes in the blood serum of patients with hyperplastic and early neoplastic lesions in the ovaries. Journal of Ovarian Research, 7(1), 1-4.
LIMA, ISAÍAS. PINHEIRO, A. M. CARLOS, SANTOS, A. O. FLÁVIA. Inteliência artificial. 1. ed. Rio de Janeiro: Elsevier, 2014.
Low-Yinga, S., R. Anthony Shawa, Michael Lerouxb, Henry H. Mantscha. (2002). Quantitation of glucose and urea in whole blood by mid-infrared spectroscopy of dry films. Vibrational Spectroscopy, 28, 111-116.
Mohd Ali, J., Hussain, M. A., Tade, M. O., & Zhang, J. (2015). Artificial Intelligence techniques applied as estimator in chemical process systems – A literature survey. Expert Systems with Applications. doi: 10.1016/j.eswa.2015.03.023
Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., & Osuch, M. (2015). Comparing various artificial neural network types for water temperature prediction in rivers. Journal of Hydrology, 529, 302-315.
Song, S. Y., Lee, Y. K., & Kim, I. J. (2016). Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis. Food Chem, 190, 1027-1032. doi: 10.1016/j.foodchem.2015.06.068
Shahin, M. A. (2014). State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers. doi: 10.1016/j.gsf.2014.10.002
Yadav, J., Rani, A., Singh, V., & Murari, B. M. (2015). Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy. Biomedical Signal Processing and Control, 18, 214-227.
Irudayaraj, J. (2002). Rapid determination of vitamin C by NIR, MIR and FT-Raman techniques. J. Pharm. Pharmacol., London, v.54, n.9, p.1247-1255, 2002.
Jessen, T. E., Hoskuldsson, A. T., Bjerrum, P. J., Verder, H., Sorensen, L., Bratholm, P. S., . . . Jensen, M. A. (2014). Simultaneous determination of glucose, triglycerides, urea, cholesterol, albumin and total protein in human plasma by Fourier transform infrared spectroscopy: direct clinical biochemistry without reagents. Clin Biochem, 47(13-14), 1306-1312. doi: 10.1016/j.clinbiochem.2014.05.064
Karmowski, K. A. S., Jacek Majda, Piotr Rubisz, Stanisław Han e Andrzej Karmowski. (2014). Lipid index changes in the blood serum of patients with hyperplastic and early neoplastic lesions in the ovaries. Journal of Ovarian Research, 7(1), 1-4.
LIMA, ISAÍAS. PINHEIRO, A. M. CARLOS, SANTOS, A. O. FLÁVIA. Inteliência artificial. 1. ed. Rio de Janeiro: Elsevier, 2014.
Low-Yinga, S., R. Anthony Shawa, Michael Lerouxb, Henry H. Mantscha. (2002). Quantitation of glucose and urea in whole blood by mid-infrared spectroscopy of dry films. Vibrational Spectroscopy, 28, 111-116.
Mohd Ali, J., Hussain, M. A., Tade, M. O., & Zhang, J. (2015). Artificial Intelligence techniques applied as estimator in chemical process systems – A literature survey. Expert Systems with Applications. doi: 10.1016/j.eswa.2015.03.023
Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., & Osuch, M. (2015). Comparing various artificial neural network types for water temperature prediction in rivers. Journal of Hydrology, 529, 302-315.
Song, S. Y., Lee, Y. K., & Kim, I. J. (2016). Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis. Food Chem, 190, 1027-1032. doi: 10.1016/j.foodchem.2015.06.068
Shahin, M. A. (2014). State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers. doi: 10.1016/j.gsf.2014.10.002
Yadav, J., Rani, A., Singh, V., & Murari, B. M. (2015). Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy. Biomedical Signal Processing and Control, 18, 214-227.
Publicado
04/07/2016
Como Citar
HESSE, Henrique; FROZZA, Rejane; CORBELLINI, Valeriano; REUTER, Cézane; BURGOS, Miria.
The use of Artificial Neural Network for lipid and glycaemic profiles quantification through infrared spectroscopy. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 16. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 2613-2616.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2016.9910.
